Approximately 1% of the world's population suffers from epilepsy which is the second most common neurological disorder and is characterized by seizures. Reliable seizure detection and prediction are therefore important for not only improving the lives of epileptic patients, but also in assisting the epileptologists in marking seizures in the electroencephalogram (EEG) recordings. An apparatus that can detect or predict seizures can be used in a closed-loop therapy system to deliver an anti-epileptic drug or other therapy, such as neurostimulation, as needed.
Therefore, there is a current need for designing algorithms for a wearable or an implantable device that can reliably detect or predict seizures with low computational complexity. In particular, the algorithm should require low power consumption and low hardware cost when implemented in an apparatus that can detect or predict seizures.